You must be aware of our previous posts such as –MOSCRACKandWPA Cracker. We now have anotheropen sourceoffering that is NOT acracker, but acryptanalysissuite that is written in CUDA – a parallel computing architecture.

CloudCrack is based upon aPollard’s Rhofactoringhybrid with an updatedBrent cycle finder, and includes performance optimizations to the traditional Rhofactoringmethod. The massively parallel design of the NVIDIA GPU architecture lends itself quite well to Pollard’s Rho, and the reduction sieve performance enhancements added with CloudCrack can reduce by several orders of magnitude the size of the keyspace required to search for a successful brute force attack against a large RSA target modulus n.

The only thing that will hurt us is that our small time home computers will not be able to support thisapplication. To run thisopen sourcesoftware, you will need a Fermi capable GPU such as a GeForce GTS 450 or GTX 460 series, and aLinux-based NVIDIA CUDA (3.2 or better) development environment. RHEL 5.5 or Fedora 13 is preferred for maximum compatibility with future EC2 parameterized launch instances. The most recent generation of consumer CUDA GPUs from NVIDIA contain hundreds of cores, each core of which can be utilized as aconcurrentRhofactoringthread (the GTS 450 has 192 hardware cores; the GTX 460 has 336; and, the M2050/2060 Tesla series have 448 cores each). You ofcourse could rent them from Amazon, etc.

All this certainly sounds awesome and we are sure that there will be a spurt in cracking services. Thissoftwarecurrently comes in two versions -

REVA, which implements the Greatest Common Denominator (gcd) function on the GPU itself; currently there is a bug in the Montgomery math routines in the REVA gcd however.

REVBincludes reduction sieve performance enhancements but with the gcd function implemented on the host CPU, which requires about 25% of the PCIe bus bandwidth to shuffle targets from the GPU to the host CPU for the gcd test.

We can expect aREVCsoon, which will include all of the performance enhancements inherent to the REVB fork, with a GPU-localized gcd like the architecture of REVA.

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